Improving AI tools for better healthcare for everyone
Official title: Trustworthy Machine Learning for Equitable Healthcare
Funded at University of Pennsylvania · NIH-11070138 · Data updated 2026-11-12
This study is working on improving AI tools to help doctors diagnose infections like tuberculosis and sepsis more accurately, especially for patients from different backgrounds, so everyone gets the best care possible.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | September 17, 2024 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $53,974 |
| NIH project number | 1F30MD020264-01 |
| Project ID | NIH-11070138 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with infectious diseases, particularly those from historically marginalized backgrounds.
✕Less likely to help
Patients who do not have infectious diseases or those who are not part of underrepresented populations may not benefit from this research.
What this research studies
This research focuses on enhancing artificial intelligence (AI) and machine learning (ML) models to support clinical decision-making in diagnosing infectious diseases like tuberculosis and sepsis. It aims to address the biases present in current AI algorithms that often lead to inaccurate diagnoses, particularly for minority patients. By developing more trustworthy and interpretable AI systems, the project seeks to ensure that these tools perform reliably across diverse patient populations. The methodology includes algorithmic innovations and practical solutions to improve the calibration and representation of minority patients in training datasets.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and equitable healthcare outcomes for patients from all backgrounds.
How similar studies have gone: Other research has shown promise in improving AI algorithms for healthcare, but this approach specifically targets the reduction of bias in clinical settings, making it a novel endeavor.
Where this research is happening
-
University of Pennsylvania
Philadelphia, United States Active
Researchers
- Principal investigator: Yao, Michael Steven Yu-Shuan — University of Pennsylvania
- Study coordinator: Yao, Michael Steven Yu-Shuan
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.